createGP {mlegp} | R Documentation |
creates a Gaussian process gp
object
createGP(X, Z, beta, a, meanReg, sig2, nugget, param.names = 1:dim(X)[2], constantMean = 1)
X |
the design matrix |
Z |
output obtained from the design matrix X , as a vector or a 1-column matrix |
beta |
vector of correlation coefficients |
a |
vector of smoothness parameters in the correlation function (if mlegp is used, these will be 2) |
meanReg |
the constant mean if constantMean = 1, otherwise the regression coefficients of the mean function such that meanReg pre-multiplied by (1 X) will produce the mean matrix |
sig2 |
the unconditional variance of the Gaussian process |
nugget |
the constant nugget or a vector of length nrow(X) corresponding to the diagonal nugget matrix |
param.names |
optional vector of parameter names (with length equal to ncol(X) |
constantMean |
1 if the Gaussian process has a constant mean; 0 otherwise |
an object of class gp
that contains the following components:
Z |
matrix of observations |
numObs |
number of observations |
X |
the design matrix |
numDim |
number of dimensions of X |
constantMean |
1 if GP has a constant mean; 0 otherwise |
mu |
the mean matrix |
Bhat |
mean function regression coefficients |
beta |
correlation parameters |
a |
smoothness parameters in correlation function |
sig2 |
unconditional variance of predicted expected output |
params |
vector of parameter names, corresponding to columns of X |
invVarMatrix |
inverse var-cov matrix |
nugget |
constant nugget or vector corresponding to the diagonal nugget matrix |
loglike |
the log likelihood of the observations |
cv |
results from cross-validation, where
cv[,1] are the cross-validated predictions
cv[,2] are the variances of the cross-validated predictions |
this function is called by mlegp
and should not be called by the user
Garrett M. Dancik dancikg@nsula.edu
http://users.nsula.edu/dancikg/mlegp/